Introduction

This file takes a side step from the other analyses of consumption to combine both soy and beef trase data in order to link consumption of China and the EU with Brazil’s river basin. We generate a bar graph for both China and the EU in 2015-2017, superimposing soy and beef in the same bar graph.

Note that you should not derive the total water use for beef here due to the status of the AGGREGATED municipalities for which there is no river basin.

Water use summary

We first look at the overall virtual water trade via products to China and the EU.

## # A tibble: 12 × 7
##    year  economic_bloc product km3_gw km3_bw Mt_volume km3_tot
##    <chr> <chr>         <chr>    <dbl>  <dbl>     <dbl>   <dbl>
##  1 2015  CHINA         Beef       228   1.56      0.41     230
##  2 2015  CHINA         Soy         70   0.36     37.1       70
##  3 2015  EU            Beef        61   0.76      0.18      62
##  4 2015  EU            Soy         26   0.16     13.4       26
##  5 2016  CHINA         Beef       274   1.99      0.55     276
##  6 2016  CHINA         Soy         67   0.66     34.4       68
##  7 2016  EU            Beef        58   0.77      0.18      59
##  8 2016  EU            Soy         26   0.24     12.8       26
##  9 2017  CHINA         Beef       373   2.57      0.72     376
## 10 2017  CHINA         Soy         85   0.52     48         86
## 11 2017  EU            Beef        52   0.71      0.16      53
## 12 2017  EU            Soy         23   0.12     12.4       23

We then look at the total water use that is linked to specific basins and look at product and water type (green and blue).

Note here that the “AGGREGATED” municipalities in the beef trade data have to be removed and so cannot be linked to a specific basin. We show the volume of aggregated flows below in case we want to report them.

## # A tibble: 6 × 5
## # Groups:   year [3]
##   year  economic_bloc volume pasture_gw_km3 tot_km3_zanetti
##   <chr> <chr>          <dbl>          <dbl>           <dbl>
## 1 2015  CHINA          1074.         0.430         0.00374 
## 2 2015  EU              253.         0.117         0.000928
## 3 2016  CHINA          1726.         0.679         0.00586 
## 4 2016  EU              200.         0.0898        0.000737
## 5 2017  CHINA          2285.         1.12          0.00837 
## 6 2017  EU              189.         0.0899        0.000687

We need to add this information in the text when describing the river basins that are the source of water for beef.

Meso basin boundaries

We first show the breakdown per meso basin (e.g. sub-basins of the Amazone River). The code is slightly more complex because we need to ensure that all the sub-basins are always shown, even in cases where there is no soy or beef exports.

This information could appear in the supplemental material

Then we get a summary of the breakdown of water use per meso river basin and year, and percent of macro basin (e.g. Amazon, São Francisco, etc.).

The results are ranked by alphabetical order of the macro basin.

The only issue needing a fix is to rank the meso basins in alphabetical order (we will not publish these results at this stage). Recall that “AGGREGATE” are not include and so the sum will not match 100 water volumes traded.

Then focus on blue water use only, from irrigation and cattle.

### Macro basin boundaries

Considering all water sources (green + blue)

We then look at total volumes sourced from macro basins.

For the year 2017:

For the year 2016:

For the year 2015:

We then calculate the breakdown of water appropriation per macro basin and link to water scarcity values from ANA. These results consider all water appropriated (green + blue) for all products.

The table below provides the relative proportions of water sourced from the different macro basins of Brazil:

We then check the links of each country to water scarcity considering all water use and products.

We note that in 2017:

  • Virtual water imported into China was associated with high (5.5%) or critical (5.1%) water scarcity, compared to average (65%) and low (25%)
  • Virtual water imported into the EU was associated with high (6%) or critical (6%) water scarcity compared to average (63%) and low (25%)

We then look at individual products considering all water appropriated and link to water scarcity according to ANA.

The table below gives the breakdown per basin of water appropriated for each of the products showing which commodity appropriated most water in which basin (i.e. sum of percentage is 100 in each macro basin):

According to the above table, and looking specifically at 2017 we see that:

  • There are large percentage of water import due to green water for beef representing a large portion of appropriation in basins (for both China and EU imports)
  • Blue water for beef and soy is always a small percentage of overall water appropriation in river basins; that’s why it is also important to just look at blue water

Then we can check which commodity is at highest risk of water scarcity, considering all water appropriated and all basins:

I am not sure that the above is actually worth discussing since it is overwhelmingly green water, in 2017:

  • Virtual green water imports to China were associated with soy at high (19%) and critical (8.5%) water scarcity; beef green water imports were associated with high water scarcity (6%) and critical (14%)
  • Virtual green water imports to the EU were associated with soy at high (3%) and critical (3%) water scarcity; beef green water imports was assocaited with high water scarcity (1%) and critical (1%)

Considering blue water sources only

Then we look at blue water use assigned to China and EU imports of soy and beef.

For the year 2017:

For the year 2016:

For the year 2015:

We then calculate the breakdown of blue water appropriation per macro basin and link to water scarcity values from ANA. These results consider only blue water appropriated for all products.

The table below provides the relative proportions of water sourced from the different macro basins of Brazil:

And then we focus the analysis purely on the water scarcity status (not basin)

We note that in 2017:

  • About 18% of all virtual blue water imported into China were associated with high (6%) or critical (12%) water scarcity, compared to average (64%) and low (18%)
  • About 53% of all virtual water imported into EU were associated with high (42%) or critical (13%) water scarcity compared to average (41%) and low (8%)

We then look at individual products considering only blue water appropriated and link to water scarcity according to ANA.

The table below gives the breakdown per basin of blue water appropriated for each of the products showing which commodity appropriated most water in which basin (i.e. sum of percentage is 100 in each macro basin):

If we looks specifically at high and critical water scarcity we note that in 2017:

  • China appropriated more water for beef in high water scarce basins (Uruguai, Atlantico Sul, Atlantico Leste), but more water for soy in critical basins (São Francisco, Parnaíba)
  • EU was the same.

Mapping blue water sources for trade

Then we can plot the total amount of blue water (for both soy and beef) that is linked to imports into China and the EU (keeping in mind that the scarcity benchmark is done later on in another map).

2017 CHINA:

2017 EU:

2016 CHINA:

2016 EU:

2015 CHINA:

2015 EU: